Light Pollution and Moth Diversity Analysis

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Light Pollution and Moth Diversity Analysis

ISEF Category: Earth and amp; Environmental Sciences

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Subcategory: Environmental Effects on Ecosystems  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Moths can disappear from a habitat long before people notice. Streetlights, porch lights, and city glow can change what comes to a trap, and what survives nearby. If you can measure those shifts, you can turn a night sky problem into real data.

What Is It?

This project asks a simple question with a tricky answer, how does artificial light at night change moth diversity? Moths are a good test group because many species respond strongly to light. Some gather near lamps. Some avoid lit areas. Some may become easier prey when they are exposed.

You can think of it like noise in a room. If the room gets louder, the quiet voices are harder to notice. Light pollution can do something similar in ecosystems. Instead of sound, the extra glow changes behavior, feeding, mating, and survival. Your job is to measure whether moth communities shift as night-light levels rise.

The project combines field ecology, computer vision, and satellite data. A Raspberry Pi UV-light trap helps collect images of moths. A smartphone camera can document each catch. YOLOv8, a machine learning model for object detection, can sort moths from the background and help estimate abundance or diversity. VIIRS night-lights data from satellites gives you a way to compare local trapping results with broader light pollution levels.

Why This Is a Good Topic

This is a strong science fair topic because you can test a real environmental problem with measurable variables. Light pollution affects insects, birds, and food webs, so your results connect to a bigger ecological issue. You can build a project around counts, diversity, and image-based classification, which gives you real data to analyze. A student can learn field sampling, computer vision basics, and statistics without needing to invent new hardware from scratch.

Research Questions

  • How does night-lights radiance affect moth abundance at sampling sites?
  • What is the effect of local artificial light intensity on moth species diversity?
  • Does moth body size distribution change as surrounding light pollution increases?
  • To what extent does the time of night alter moth capture rates in low-light and high-light sites?
  • Which classification accuracy do you get when YOLOv8 sorts moth images from different trap backgrounds?
  • How does nearby land cover change the relationship between VIIRS radiance and moth diversity?

Basic Materials

  • Raspberry Pi single-board computer with camera module
  • UV light trap or UV LED setup designed for insect sampling
  • Smartphone camera with manual exposure control
  • Tripod or fixed phone mount
  • White collection sheet or trap surface for imaging
  • Field notebook or data sheet
  • GPS-enabled phone for site coordinates
  • Digital kitchen scale or ruler for simple specimen measurements
  • External battery pack or field power source
  • Weather app or portable thermometer for site notes.

Advanced Materials

  • Raspberry Pi with compatible camera and storage card
  • UV trap housing with standardized sampling entrance
  • DSLR or mirrorless camera with macro lens
  • Portable color reference card for image calibration
  • USB microscope or stereo microscope for species-level checks
  • Environmental sensors for temperature, humidity, and cloud cover
  • Access to preserved reference specimens or entomology collection for model training
  • Higher-capacity field battery or regulated power supply
  • Calipers for morphological measurements
  • Computer with GPU access for YOLOv8 training and validation.

Software & Tools

  • Python: Organizes image files, runs analysis scripts, and handles statistical testing.
  • YOLOv8: Detects moths in trap images and helps estimate counts or image quality.
  • ImageJ: Measures size, brightness, and color features from standardized photos.
  • QGIS: Maps trap sites and compares results with land cover or light data.
  • NASA Earthdata Search: Helps you find and download VIIRS night-lights datasets for your study area.

Experiment Steps

  1. Define your response variable, such as moth abundance, species richness, or classifier-based diversity estimates.
  2. Choose sampling sites that span a clear gradient of night-light exposure.
  3. Plan a consistent trapping and imaging method so every site produces comparable data.
  4. Build a labeling strategy for moth images, including how you will separate background, individuals, and uncertain cases.
  5. Set up a comparison between local trap data and satellite-derived VIIRS radiance values.
  6. Decide which statistical test or model will match your question, then plan how you will check assumptions and error rates.

Common Pitfalls

  • Using traps that differ in brightness or design, which makes light level and trap bias impossible to separate.
  • Mistaking trap catch for true population size, which can overstate the ecological effect of light pollution.
  • Training YOLOv8 on blurry or inconsistent photos, which lowers detection accuracy and creates noisy counts.
  • Comparing sites with different weather, habitat, or moon phase, which can hide the light-pollution pattern.
  • Mixing species-richness changes with simple abundance changes, which can lead you to claim the wrong biological effect.

What Makes This Competitive

A strong version of this project does more than count insects near lights. You would control for habitat, weather, and trap design, then test whether the light-pollution signal still holds. You could also compare image-based diversity estimates against human identification, which adds a stronger validation step. A careful link between field sampling and satellite radiance data can turn a local study into a cleaner ecological analysis.

Project Variations

  • Compare moth catch rates across suburban, rural, and protected dark-sky sites instead of just one neighborhood.
  • Train the classifier to separate moth families or wing-pattern groups, then test whether diversity trends change by taxonomic resolution.
  • Add habitat variables such as tree cover or distance to water, then model whether those features weaken or strengthen the light-pollution effect.

Learn More

  • NASA Earthdata Search: Find VIIRS night-lights data and related Earth observation products for mapping artificial light.
  • NOAA National Centers for Environmental Information: Explore nighttime light and environmental datasets through government data portals.
  • USGS Earth Resources Observation and Science Center: Look for remote sensing guides and land-cover data that can support your site comparisons.
  • PubMed: Search for review articles on artificial light at night, moth behavior, and insect ecology.
  • MIT OpenCourseWare: Use free courses in ecology, statistics, or machine learning to support your analysis design.
  • Journal of Insect Conservation: Search recent papers on light pollution, moths, and insect monitoring methods.
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